Video Education and Behavior Contract to Improve Outcomes After Renal Transplantation (VECTOR): A Randomized Controlled Trial
Bibliographic record
Abstract
Sub-optimal adherence to immunosuppressant medications reduces graft survival for kidney transplant recipients and adherence-enhancing interventions are resource and time intensive. We performed a multi-center randomized controlled trial to investigate the impact of an electronically delivered intervention on adherence. Of 203 adult kidney transplant recipients who received a de novo kidney transplant n = 173 agreed to participate (intent-to-treat population) and were randomized to the intervention (video education plus behavior contract n = 91) or the control (standard education, n = 82). No significant differences were found between the groups for medication adherence measured by the Basel Assessment of Adherence to Immunosuppressive Medications Scale, intrapatient variability in tacrolimus levels, time in therapeutic range for any immunosuppressant, knowledge, self-efficacy, QOL, or hospitalizations. Among a subgroup of 64 participants randomized to the intervention group who completed a post-intervention questionnaire, two-thirds (67%, n = 43) reported watching at least 80% of the videos and 58% (n = 37) completed the electronic goal setting exercise and adherence contract. An autonomous goal setting exercise and electronic behavioural contract added to standard of care did not improve any outcomes. Our findings reiterate that nonadherence in transplantation is a difficult multifactorial problem that simple solutions will not solve. Trial registration number NCT03540121.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".